Blink: Not Your Father's Database!

The Blink project’s ambitious goals are to answer all Business Intelligence (BI) queries in mere seconds, regardless of the database size, with an extremely low total cost of ownership. It takes a very innovative and counter-intuitive approach to processing BI queries, one that exploits several disruptive hardware and software technology trends. Specifically, it is a new, workload-optimized DBMS aimed primarily at BI query processing, and exploits scale-out of commodity multi-core processors and cheap DRAM to retain a (copy of a) data mart completely in main memory. Additionally, it exploits proprietary compression technology and cache-conscious algorithms that reduce memory bandwidth consumption and allow most SQL query processing to be performed on the compressed data. Ignoring the general wisdom of the last three decades that the only way to scalably search large databases is with indexes, Blink always performs simple, “brute force” scans of the entire data mart in parallel on all nodes, without using any indexes or materialized views, and without any query optimizer to choose among them. The Blink technology has thus far been incorporated into two products: (1) an accelerator appliance product for DB2 for z/OS (on the “mainframe”), called the IBM Smart Analytics Optimizer for DB2 for z/OS, V1.1, which was generally available in November 2010; and (2) the Informix Warehouse Accelerator (IWA), a software-only version that was generally available in March 2011. We are now working on the next generation of Blink, called BLink Ultra, or BLU, which will significantly expand the “sweet spot” of Blink technology to much larger, disk-based warehouses and allow BLU to “own” the data, rather than copies of it.

[1]  Alexander Zeier,et al.  HYRISE - A Main Memory Hybrid Storage Engine , 2010, Proc. VLDB Endow..

[2]  Kai-Uwe Sattler,et al.  Architecture of a Highly Scalable Data Warehouse Appliance Integrated to Mainframe Database Systems , 2011, BTW.

[3]  Andrew A. Chien,et al.  The future of microprocessors , 2011, Commun. ACM.

[4]  Michael Stonebraker,et al.  C-Store: A Column-oriented DBMS , 2005, VLDB.

[5]  David J. DeWitt,et al.  Weaving Relations for Cache Performance , 2001, VLDB.

[6]  Frederick Reiss,et al.  Constant-Time Query Processing , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[7]  Alexander Zeier,et al.  SIMD-Scan: Ultra Fast in-Memory Table Scan using on-Chip Vector Processing Units , 2009, Proc. VLDB Endow..

[8]  Garret Swart,et al.  How to wring a table dry: entropy compression of relations and querying of compressed relations , 2006, VLDB.

[9]  Alfons Kemper,et al.  HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[10]  Frederick Reiss,et al.  Main-memory scan sharing for multi-core CPUs , 2008, Proc. VLDB Endow..

[11]  Martin Grund,et al.  A demonstration of HYRISE , 2011, VLDB 2011.

[12]  Kai-Uwe Sattler,et al.  Autonomous workload-driven reorganization of column groupings in MMDBS , 2011, 2011 IEEE 27th International Conference on Data Engineering Workshops.

[13]  Marcin Zukowski,et al.  MonetDB/X100: Hyper-Pipelining Query Execution , 2005, CIDR.

[14]  David J. DeWitt,et al.  How to barter bits for chronons: compression and bandwidth trade offs for database scans , 2007, SIGMOD '07.

[15]  Ryan Johnson,et al.  Row-wise parallel predicate evaluation , 2008, Proc. VLDB Endow..

[16]  Frank B. Schmuck,et al.  GPFS: A Shared-Disk File System for Large Computing Clusters , 2002, FAST.